Distribution control systems must support both periodic functions and aperiodic functions that send and receive data over one or more common data buses. Sending messages on a common data bus or scheduling tasks on a common processor are canonical examples. To ensure guaranteed latencies, that is, network delays, for closed loop periodic control functions such as sensor read, control, actuator write, and the like, static scheduling techniques are presently used to produce a timeline where known data are transmitted at predefined times, leaving a sequence of gaps for aperiodic message transmission.
There are currently no scheduling models that allow efficient platforms to be built that predictably support both types of applications. For critical aperiodic functions, such as event-triggered ones, with deadline requirements, such as pilot input, target tracking, alarm signals and the like, sufficient bandwidth to allow for worst case event arrival is statically reserved. This results in an over-engineered system and recurring hardware costs. For non-critical aperiodic functions such as internet connections, voice or video, for example, system design has heretofore been by trial-and-error. There has been no way to predict aperiodic message latencies.
The very broad concept of latency measurements in a communication system is taught Link et al. U.S. Pat. No. 6,012,096 relates to a method for network latency in which data packets are transmitted between the users. Skurdal et al. U.S. Pat. No. 6,161,009 uses a control circuit to turn a transmitter on and off, measuring the latency time and using the data in subsequent transmission of data.
Dean et al. U.S. Pat. No. 6,332,178 discloses a method for estimating statistics of properties of transactions processed by a memory sub-system of a computer system. This broadly discloses statistical sampling. Goldberg U.S. Pat. No. 5,767,785 discloses a different method for generating prediction recommendations for signals. Grochowski et al. U.S. Pat. No. 6,035,389 teaches a latency vector system using a register latency table. Borella et al U.S. Pat. No. 6,182,125 discloses a determined network latency method. Hershey et al. U.S. Pat. No. 5,375,070, Chuah U.S. Pat. No. 6,115,390, Black et al. U.S. Pat. No. 6,038,599, Foore et al. Publication 2001/002119, and Skene et al. Publication 2001/0052016 are other patents that are generally related to network latency.
The general problem of estimating aperiodic latencies in complex systems is enormously difficult, especially for multi-class traffic, which, without special restrictions, is not well understood. A first area of concern are those in which steady state approximations can be made, without estimations for transient behavior.
A mathematical analysis of aperiodic message latency distributions is rarely tractable, so it must be estimated using empirical data. For estimating latency distributions, one approach is to construct an empirical distribution function (EDF) using (simulation) data. Another approach is to construct an (approximate) analytic model which would be solved numerically. In the former, PDF's are constructed using a sample of k independent and identically distributed (iid) latency values, {x1, x2, . . . , xk. A typical value for k is 500 or more, which is difficult to process but which is necessary to get an appropriate set of values.
It would be of great advantage in the art if a greater understanding of steady state approximations of latencies could be achieved.
It would be another great advance in the art if a sampling point strategy could be devised that would produce a compact estimate of aperiodic latency distribution.
Other advantages will appear hereinafter.